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文章基本信息

  • 标题:Reinforcement Learning Applied to a Cryptocurrency Portfolio in a Complexity Environment
  • 本地全文:下载
  • 作者:Daniel Sousa Barra ; Helberte França Almeida ; Rafael Jasper Feltrin
  • 期刊名称:Revista Economia Ensaios
  • 印刷版ISSN:1983-1994
  • 出版年度:2021
  • 卷号:36
  • 期号:1
  • 页码:1-16
  • DOI:10.14393/REE-v36n1a2021-50850
  • 出版社:EDUFU
  • 摘要:Recently, cryptocurrencies have been used as financial assets and have presented positive returns, albeit their volatility is high. This paper aims to elaborate a hypothetical cryptocurrency portfolio and to do so, employs machine learning and an optimization algorithm to define the ideal amount to be allocated in each asset. The results show the hypothetical portfolio presents superior returns and lesser volatility compared to other allocation strategies.
  • 其他摘要:Recently, cryptocurrencies have been used as financial assets and have presented positive returns, albeit their volatility is high. This paper aims to elaborate a hypothetical cryptocurrency portfolio and to do so, employs machine learning and an optimization algorithm to define the ideal amount to be allocated in each asset. The results show the hypothetical portfolio presents superior returns and lesser volatility compared to other allocation strategies.
  • 关键词:Cryptocurrencies; Reinforcement Learning; Return on Assets.
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